A fast and efficient segmentation scheme for cell microscopic image.

Microscopic cellular image segmentation schemes must be efficient for reliable analysis and fast to process huge quantity of images. Recent studies have focused on improving segmentation quality. Several segmentation schemes have good quality but processing time is too expensive to deal with a great number of images per day. For segmentation schemes based on pixel classification, the classifier design is crucial since it is the one which requires most of the processing time necessary to segment an image. The main contribution of this work is focused on how to reduce the complexity of decision functions produced by support vector machines (SVM) while preserving recognition rate. Vector quantization is used in order to reduce the inherent redundancy present in huge pixel databases (i.e. images with expert pixel segmentation). Hybrid color space design is also used in order to improve data set size reduction rate and recognition rate. A new decision function quality criterion is defined to select good trade-off between recognition rate and processing time of pixel decision function. The first results of this study show that fast and efficient pixel classification with SVM is possible. Moreover posterior class pixel probability estimation is easy to compute with Platt method. Then a new segmentation scheme using probabilistic pixel classification has been developed. This one has several free parameters and an automatic selection must dealt with, but criteria for evaluate segmentation quality are not well adapted for cell segmentation, especially when comparison with expert pixel segmentation must be achieved. Another important contribution in this paper is the definition of a new quality criterion for evaluation of cell segmentation. The results presented here show that the selection of free parameters of the segmentation scheme by optimisation of the new quality cell segmentation criterion produces efficient cell segmentation.

[Alcoholism, anxiety and depression.]. / Alcoolisme, anxiété et dépression.

In this article, the authors examine the links between alcoholism and psychiatric disorders. They estimate that alcohol addiction is rarely a pathology that appears in an isolated fashion. North-american studies conducted within the general population (Epidemiological Catchment Area (ECA), National Comorbidity Study) have confirmed the frequent association of psychiatric disorders and alcoholism (Regier et al., 1990). The authors conclude that depression and anxiety are the two major psychiatric disorders of alcoholism. They suggest that the treatment of anxiety as well as depression be integrated to that of alcoholism.

Segmentation of color images from serous cytology for automated cell classification.

OBJECTIVE: To design an automated system for the classification of cells based on analysis of serous cytology, with the aim of segmenting both cytoplasm and nucleus using color information from the images as the main characteristic of the cells. STUDY DESIGN: The segmentation strategy uses color information coupled with mathematical morphology tools, such as watersheds. Cytoplasm and nuclei of all diagnostic cells are retained; erythrocytes and debris are eliminated. Special techniques are used for the separation of clustered cells. RESULTS: A large set of cells was assessed by experts to score the segmentation success rate. All cells were segmented whatever their spatial configurations. The average success rate was 92.5% for nuclei and 91.1% for cytoplasm. CONCLUSION: This color information-based segmentation of images of serous cells is accurate and provides a useful tool. This segmentation strategy will improve the automated classification of cells.

Automatic morphological sieving: comparison between different methods, application to DNA ploidy measurements.

The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)). Performances were evaluated by reference to interactive sorting. The results obtained for the three methods concerning the percentage of debris and aggregates automatically removed reach 63, 75 and 85% for MM, MA and NN methods, respectively, with false positive rates of 6, 21 and 25%. Information about DNA ploidy abnormalities were globally preserved after automatic elimination of debris and aggregates by MM and MA methods as opposed to NN method, showing that automatic classification methods can offer alternatives to tedious interactive elimination of debris and aggregates, for DNA ploidy measurements of archival tumours.

[The long-term course of depression (epidemiology and clinical aspects)]. / Evolution à long terme des dépressions (épidémiologie et clinique).

Depression recurs in three quarters of cases; it is therefore necessary to undertake long-term studies in order to understand the clinical and epidemiological implications. Current classifications schematically distinguish depressive episodes according to their more or less permanent and complete semiological expression (at least five symptoms over at least two weeks for a major depressive episode, versus at least two criteria for the greater part of the time over at least two years) or their time-scale (isolated or recurrent episodes; recurrent brief depressive episodes...). The terminology of therapeutic strategies is based on the temporal definitions of the depressive process. Thus one speaks of curative treatment during the acute phase of the illness (two months), maintenance treatment during recurrence (four to six months), and prophylaxis against later possible recurrences (more than six months). Epidemiological findings emphasize the importance not only of recurrence of depression (50% in the year following an index episode), but also that of becoming chronic (20%), of partial remissions (15 to 20%), and the "bipolarisation" of a unipolar illness (10 to 15%). Finally, certain risk factors for recurrence have been identified. The most important of these is a large number of previous depressive episodes.